What happens when we can no longer trust the performance claims of AI model creators?

That’s the challenge driving LayerLens, an innovative platform bringing transparency and practical evaluation to the rapidly evolving AI landscape.

In this episode, host Alex Kehaya sits down with Archie Chaudhury, Co-founder of LayerLens, to explore how blockchain-powered benchmarking can cut through the hype and deliver verifiable, real-world AI performance metrics. Unlike traditional academic benchmarks focused on abstract intelligence scores, LayerLens enables practical evaluations that reflect the real use cases businesses care about.

Archie explains how the platform’s integration with EigenLayer makes every step of the evaluation process transparent — every question, every answer, and whether the model got it right or wrong — removing the need to rely on self-reported claims from model creators.

We also dig into LayerLens’ vision for democratizing benchmark creation, empowering subject matter experts across industries to design their own domain-specific evaluations. This community-driven model builds a diverse library of practical tests that can better reflect the complexity of real-world challenges.

From decentralized evaluation to the broader “Open Intelligence” stack — decentralized data, compute, training, and inference — this conversation reveals how LayerLens is shaping the future of AI accountability at a critical moment in the technology’s evolution.

Whether you’re building with AI, investing in the space, or simply curious about how we can trust the systems making decisions on our behalf, this episode pulls back the curtain on what’s really under the hood of your favorite AI models.

🔗 Learn more: layerlens.ai | X (Twitter)

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